A task-specific encoding algorithm for RNAs and RNA-associated interactions based on convolutional autoencoder

Abstract RNAs play essential roles in diverse physiological and pathological processes by interacting with other molecules (RNA/protein/compound), and various computational methods are available for identifying these interactions. However, the encoding features provided by existing methods are limit...

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Veröffentlicht in:Nucleic acids research Jg. 51; H. 21; S. e110
Hauptverfasser: Wang, Yunxia, Pan, Ziqi, Mou, Minjie, Xia, Weiqi, Zhang, Hongning, Zhang, Hanyu, Liu, Jin, Zheng, Lingyan, Luo, Yongchao, Zheng, Hanqi, Yu, Xinyuan, Lian, Xichen, Zeng, Zhenyu, Li, Zhaorong, Zhang, Bing, Zheng, Mingyue, Li, Honglin, Hou, Tingjun, Zhu, Feng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: England Oxford University Press 27.11.2023
Schlagworte:
Algorithms
Computational Biology - methods
Methods
RNA - genetics
Software
ISSN:0305-1048, 1362-4962, 1362-4962
Online-Zugang:Volltext
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Zusammenfassung:Abstract RNAs play essential roles in diverse physiological and pathological processes by interacting with other molecules (RNA/protein/compound), and various computational methods are available for identifying these interactions. However, the encoding features provided by existing methods are limited and the existing tools does not offer an effective way to integrate the interacting partners. In this study, a task-specific encoding algorithm for RNAs and RNA-associated interactions was therefore developed. This new algorithm was unique in (a) realizing comprehensive RNA feature encoding by introducing a great many of novel features and (b) enabling task-specific integration of interacting partners using convolutional autoencoder-directed feature embedding. Compared with existing methods/tools, this novel algorithm demonstrated superior performances in diverse benchmark testing studies. This algorithm together with its source code could be readily accessed by all user at: https://idrblab.org/corain/ and https://github.com/idrblab/corain/. Graphical Abstract Graphical Abstract
Bibliographie:ObjectType-Article-1
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The authors wish it to be known that, in their opinion, the first two authors should be regarded as Joint First Authors.
ISSN:0305-1048
1362-4962
1362-4962
DOI:10.1093/nar/gkad929